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Record W4392397285 · doi:10.1109/toh.2024.3371389

Analog Position Estimation for Enhanced Stability and Fidelity of Haptic Systems

2024· article· en· W4392397285 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Haptics · 2024
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEncoderRotary encoderIntegratorHaptic technologyComputer scienceHigh fidelityPosition (finance)Latency (audio)FidelityBenchmark (surveying)SIGNAL (programming language)Stability (learning theory)Control theory (sociology)SimulationEngineeringArtificial intelligenceBandwidth (computing)

Abstract

fetched live from OpenAlex

In this paper, we propose three methods to compute low-latency analog position where two of them fuse encoder and rate gyro signals. While one method is based on gyro with bias correction using encoder information, the other one is encoder-referenced combined with a resettable integrator to minimize the staircase form of encoder signals. Experiments on a one degree-of-freedom haptic simulation system have shown that a low-latency analog position with an accuracy over 98% compared to the sampled encoder signal can be obtained. The analog position signals are then utilized to produce analog viscoelastic virtual environments to assess and benchmark the proposed methods through uncoupled stability and perceived fidelity tests. The results have shown that a virtual stiffness range larger than 400% can be obtained with enhanced fidelity compared to common digital implementations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.901
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.249
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it